sasirekha spectrum sensing

8/19/2019 Sasirekha Spectrum Sensing

1/16

Spectrum Sensing in Emergency Cognitive Radio Ad

Hoc Networks (CRAHNs) : A Multi!ayer Approac"

Sasirekha GVK,,Supervisor: Prof. Jyotsna Bapat, IIIT Bangalore

Reuire!ents of "!ergen#y $R%&'s:

# Accuracy

#Resource e$$iciency

#!ow latency in t"e delivery o$ packets%

# Adaptive to varying num&er o$ S's%# Adaptive to varying SNR conditions%

#'ni$orm &attery consumption

#Resilience to yantine attacks

SNR

*"res"old

Sensing

Mec"anism!ocal

decisions% accuracy

,

+usion

Rule

Num&er ,$

Sensing

S's

Sensing

time+re-uency

o$ sensing

P&( )I'K

.lo&al

decisions%

accuracy

,

Perfor!an#e


2/16

!iterature survey

$olla*orative spe#tru!sensing

1. Amir Ghasemi and Elvino S. Sousa,

2. Wei Zhang, Rajan K. Mallik, Khaled Ben Leaie!

".#lan$%

&. L. #hen, '. Wang, S. Li,

/0 1un$ei C"en

Static2Reactivemet"ods using3,R4 &ased $usion%Civilian Networks

Considering onlysome parameters$or optimiation

$ognitive Ra+io %+ ho#'etorks

(an ). Ak%ildi*, Won+eol Lee, Kaushik R. #ho-dhur%,

5rotocol stack%routing% transportand "ig" levelarc"itecture

"!ergen#y 'etorks Adaptive Ad"oc +ree and 6ireless Communications Re-uirements ingeneral

I""" Stan+ar+s 7EEE 890 (S"ell Hammer) Regional AreaNetworks in *;&and

,ur proposal proactive% dynamic% !R* &ased (&etter immunity against yantine

attacks) meeting sensing re-uirements $or emergency networks


3/16

Multi!ayer +ramework

-o#us of the resear#h

Confidence

Link Layer

Blind/

Semi-blind

Spectrum

Sensing

Averaging

AndFinal

Decision

Logic

Decision

R!Signal

"#res#old

Data Fusion

$it# opt% &

'stimator

Soft/(ardDecision

from ot#er users

Cognitive Radio

Receiver

Front 'nd

Physi#al )ayer

Adaptive"#res#olding

)roup Decision

Sensing

Sc#eduler

eing a Multi!ayer Multi5arameter optimiation pro&lem tackled as levels#!evel


4/16

Results# "sti!ation of s!allest nu!*er of sensing $Rs for a targete+ a##ura#y.

# %lgorith! for a+apting the nu!*er of sensing Ss in #hanging

environ!ents/ i.e. netork si0e an+ S'R. Propose+ for #entrali0e+ an+

+istri*ute+ spe#tru! sensing.

# %lgorith! for a+apting threshol+ for lo#al energy +ete#tion *ase+ on glo*al

group +e#isions.

# %ppli#ation of evolutionary ga!e theory for *ehavioral !o+eling of the

netork.

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 10.85

0.9

0.95

1

1.05

Qd desired

Q d a c t u a l

Qd actual versus Qd desired for various sensitivites

reference

-3%

+3%

(Pd,Pf)=0.4,0.1

(Pd,Pf)=0.5,0.15

(Pd,Pf)=0.6,0.25

(Pd,Pf)=0.76,0.4

(Pd,Pf)=0.85,0.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

iterations

Variance of energy spent,Payoff Qd, probability of sense of an SU

with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000

N o r m a l i z e d

v a l u e o f

v a r i a n c e / p r o b a b i l i t y

Normalized variance of energy spent across SUs

Probability of detect of fused data

Probability of sense of an SU

Sample Results on t"e Estimation o$ minimal no0 o$ CRS and Adaptation o$ CRs


5/16

+uture 6ork!ateral Application Areas

Cloud Networking Smart .rids


6/16

,pen 7ssues

Cognitive Radio Ad "ocNetwork

*ime sync"roniation

,ptimied !ink State Routing

Cooperative Spectrum Sensing

C omm on C on

t r ol C " ann el

Spectrum Allocation

Security #Provision of $o!!on $ontrol$hannel

#Integration of all the layers

#Se#urity Relate+ Issues

#By0antine atta#ks#Pri!ary ser "!ulation

%tta#ks#Trustorthiness1

%uthenti#ation


7/16

ack up slides


8/16

S

S

SS

$oor+inator

$entrali0e+ %r#hite#ture

S

S

S

S

S

2istri*ute+ %r#hite#ture

Cognitive Radios : Secondary Users (SUs)

Dynamic Spectrum Access •Spectrum Sensing Local & Collaborative

•Spectrum Allocation•Spectrum obility


9/16

Application Scenarios

P

>! ! 2 ?>! " ! & ! / ! 0 ?

>! r+2 ! r+?

>! r ?

Mo&ile CRAHNScenario

P P

P

•ilitary !et"or#s•Disaster anagement

$eatures:• !omadic obility• %roup Signal to !oise Rati• Collaborative Spectrum Se


10/16

P&( )I'K Perfor!an#e 3etri#s

SNR

*"res"old

Sensing

Mec"anism

C"annel

Model!ocal

decisions%

1 di

, 1 !i

+usionRule

Num&er

,$ Sensing

S's

Risk

-ro! ith S

-ro! other 4K567 Ss

5'

'sage

pattern

)evel 6 8pti!i0ation)evel 9 8pti!i0ation

Sensing

time

+re-uency

o$ sensing

@dk

@$k

7k

k F fk D dk R C Q C Q C = − +

k k I 1 R= −

( )k k k

k

JαI 1 α η

N k 0α 1,η

N

= + −

−

≤ ≤ =

*wo levels o$ optimiation


11/16

Confidence

( ) ) λ(Y β -t t t t t e1

1 λY f z

−+=−=

( )t

2t

t 1t λ

e E μ λ λ

∂

∂−=+ ) z1( ze μ2 λ λ t t t t 1t −−=+

Adaptive *"res"old

Adaptive *"res"old &ased on .roup

=ecisions


12/16

) P , P ,k ( f Q

~

f

~

d d

QQ k min K desired _d d ≥

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 10.85

0.9

0.95

1

1.05

Qd desired

Q d a c t u a l

Qd actual versus Qd desired for various sensitivites

reference

-3%

+3%

(Pd,Pf)=0.4,0.1

(Pd,Pf)=0.5,0.15

(Pd,Pf)=0.6,0.25

(Pd,Pf)=0.76,0.4

(Pd,Pf)=0.85,0.5

Group S'R5 P+;av, Pf;av5 K

Estimation o$ optimal num&er o$ CRs re-uired

$or sensing $or targeted accuracy


13/16

Behavioral Model

Intera#tion *eteen autono!ous $Rs !o+ele+

using ga!e theory

Policies

-reuen#ies to sense


14/16

Adaptive 5roactive 7mplementation

Model: Centralied Arc"itecture

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

iterations

Variance of energy spent,Payoff Qd, probability of sense of an SU

with Qd target=0.9, Group SNR 0dB, Event 2 of Table I at iterations=10000

N

o r m a l i z e d v a l u e o f v a r i a n c e / p r o b a b

i l i t y

Normalized variance of energy spent across SUs

Probability of detect of fused data

Probability of sense of an SU

( ) ( ) s a ! " !s " a s " a Jα I 1 α 1 ! = + − −

'tility +unction


15/16

=ecentralied Arc"itecture

)k ( J )k ( #a$ K ∋=

1&'stat as*+,ee

* J )k ( # K .


16/16

4 Sasire#'a %567 8yotsna 9apat7 /da0t)e #'de 2ased '( !'a&t)e 30e&t45 3e(s)(6 f' E5e6e(&7 C'6()t)e /d

,'& Net+'ks;7 CR,2!C,

sasirekha spectrum sensing

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